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When and Why to Use Shiny for Commercial Applications

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Building data products is no easy feat. At TCB Analytics, we always start with the question(s) we want to answer and then immediately identify relevant data sets. At what point though, does it make sense to build something? We discuss various examples of client work in which we used Shiny to rapidly protoype, pull data from APIs, leverage Google Vision's image recognition service and more.

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When and Why to Use Shiny for Commercial Applications

  1. 1. When and Why to Use Shiny for Commercial Applications EARL Boston 2017 -11/03/2017 Tanya Cashorali @TanyaCash21
  2. 2. 2004 201220072005 2006 20142013 2015 SPEAKER PROFILE TANYA CASHORALI @TANYACASH21 2
  3. 3. FIRST THINGS FIRST: What is a data product?
  4. 4. VALUE IS IN THE DATA ITSELF… ?
  5. 5. MYTHICAL DATA SCIENCE MACHINE RAW DATA INSIGHTS $$$
  6. 6. SUPPORT DECISION MAKING  Get used to asking yourself: “So what?”  Provides actionable insights – DO SOMETHING  Ability to ask “What-If?” questions
  7. 7. RAPID PROTOTYPING  Demonstrate value before full-scale implementation  Data scientists are able to do what they’re good at
  8. 8. WHY NOT JUST USE TABLEAU? Source: http://nandeshwar.info/data-science-2/tableau-vs-r/ Depends on… • Use Case • Audience • In-house skills • Great for aggregations and summary dashboards with drill-downs and filters
  9. 9. REPRODUCIBILITY Source: http://www.jonzelner.net/statistics/make/docker/reproducibility/2016/05/31/reproducibility-pt-1/ Knitr rocker Gitlab-CI “It’s not reproducible if it only runs on your laptop.”
  10. 10. COLLABORATION RStudio ConnectVersion Control
  11. 11. CUSTOMIZATION + FLEXIBILITY  Certain charts in Tableau require complicated workarounds (gantt, spider, chord diagrams, network viz, etc.)  Capable of machine learning, advanced statistical functions
  12. 12. BUILD PROCESS
  13. 13. VISUALIZE MODEL PREDICTIONS Whiteboard / Brainstorm / Wireframe Quickly build prototype dashboard
  14. 14. ITERATE WITH CLIENT Sketches over e-mail to reality in just several days
  15. 15. RSTUDIO MARKETING AND SALES ANALYTICS
  16. 16. GOOGLE VISION API Powerful image analysis that enables: • Logo detection • Landmark detection • Optical Character Recognition • Face and emotion detection • Classification of objects from trains to animals
  17. 17. GOOGLE VISION API Scraped 5,000+ images from the New England Patriots Facebook page Classified all pictures to understand brand / sponsor presence on Patriots page
  18. 18. EXAMPLE RESULTS Dunkin’ Donuts High Confidence Dunkin’ Donuts Low Confidence Snickers Snapchat Lord of the Rings Cancer
  19. 19. FANTASY OPTIMIZERS http://apps.fantasyfootballanalytics.net/ https://tcb-analytics.shinyapps.io/dfsnba/ Additional Reading on scaling: https://www.slideshare.net/appsilon/scaling-shiny-apps-earl-2017-san-francisco 2013 - Started with Shiny Server – ran into scaling issues 2015 - Moved to OpenCPU/Azure – ran into scaling issues in OpenCPU (greater capacity servers may have resolved, but $$$). Azure also required Java. 2017 - Moved back to Shiny (shinyapps.io w/ professional plan) – so far no scaling issues
  20. 20. WHEN TO USE SHINY SERVER PRO 20 https://support.rstudio.com/hc/en-us/articles/218294997-What-is-the-difference-between-shinyapps-io-and-Shiny-Server-Pro- Outside firewall? Data in cloud? Users must register? Shared Resources? If your organization is not OK with any of the above… you may want to consider Shiny Server Pro
  21. 21. PRODUCTION PITFALLS  Too good, too fast  Inability to keep up with demands and scale  Best to implement unit tests, documentation, deployment best practices in advance or at least be prepared to do so quickly http://schd.ws/hosted_files/user2017/7d/UseR2017_ShinyPrototypesOutOfControl.pdf Building the plane while flying
  22. 22. SPECIAL THANKS TO… 22 Grace MeyerIsaac Petersen Josh Walker Bill Carney Paul Doetsch Roger Oberg

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